基于生物特征的数字水印综述研究

生物信息学与信息安全具有高度互补性,用生物特征来确保信息安全已经成为国内外研究的热点.为此研究了利用生物特征来生成数字水印.介绍了数字水印技术所具有的安全性、隐蔽性和鲁棒性的基本特征,讨论了当前常用的水印嵌入算法,阐述了基于在线签名的生物特征数字水印生成过程,最后给出了基于生物特征的数字水印技术的应用和研究方向.     

Behavioral biometrics & continuous user authentication on mobile devices: A survey

This paper offers an up-to-date, comprehensive, extensive and targeted survey on Behavioral Biometrics and Continuous Authentication technologies for mobile devices. Our aim is to help interested researchers to effectively grasp the background in this field and to avoid pitfalls in their work. In our survey, we first present a classification of behavioral biometrics technologies and continuous authentication for mobile devices and an analysis for behavioral biometrics collection methodologies and feature extraction techniques. Then, we provide a state-of-the-art literature review focusing on the machine learning models performance in seven types of behavioral biometrics for continuous authentication. Further, we conduct another review that showed the vulnerability of machine learning models against well-designed adversarial attack vectors and we highlight relevant countermeasures. Finally, our discussions extend to lessons learned, current challenges and future trends.     

Optical demultiplexer device operating in the visible spectrum

In this paper, we present results on the use of multilayered a-SiC:H heterostructures as a device for wavelength-division demultiplexing of optical signals. These devices are useful in optical communications applications that use the wavelength division multiplexing technique to encode multiple signals into the same transmission medium. The device is composed of two stacked p-i-n photodiodes, both optimized for the selective collection of photo generated carriers. Band gap engineering was used to adjust the photogeneration and recombination rate profiles of the intrinsic absorber regions of each photodiode to short and long wavelength absorption in the visible spectrum. The photocurrent signal using different input optical channels was analyzed at reverse and forward bias and under steady state illumination. A demux algorithm based on the voltage controlled sensitivity of the device was proposed and tested. An electrical model of the WDM device is presented and supported by the solution of the respective circuit equations.     

Infrared and visible image fusion methods and applications: A survey

Infrared images can distinguish targets from their backgrounds based on the radiation difference, which works well in all-weather and all-day/night conditions. By contrast, visible images can provide texture details with high spatial resolution and definition in a manner consistent with the human visual system. Therefore, it is desirable to fuse these two types of images, which can combine the advantages of thermal radiation information in infrared images and detailed texture information in visible images. In this work, we comprehensively survey the existing methods and applications for the fusion of infrared and visible images. First, infrared and visible image fusion methods are reviewed in detail. Meanwhile, image registration, as a prerequisite of image fusion, is briefly introduced. Second, we provide an overview of the main applications of infrared and visible image fusion. Third, the evaluation metrics of fusion performance are discussed and summarized. Fourth, we select eighteen representative methods and nine assessment metrics to conduct qualitative and quantitative experiments, which can provide an objective performance reference for different fusion methods and thus support relative engineering with credible and solid evidence. Finally, we conclude with the current status of infrared and visible image fusion and deliver insightful discussions and prospects for future work. This survey can serve as a reference for researchers in infrared and visible image fusion and related fields.     

Ocular recognition databases and competitions: a survey

The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris information. In addition to providing information about an individual’s identity, features extracted from these traits can also be explored to obtain other information such as the individual’s gender, the influence of drug use, the use of contact lenses, spoofing, among others. This work presents a survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired. We also describe and discuss the most popular ocular recognition competitions (contests), highlighting the submitted algorithms that achieved the best results using only iris trait and also fusing iris and periocular region information. Finally, we describe some relevant works applying deep learning techniques to ocular recognition and point out new challenges and future directions. Considering that there are a large number of ocular databases, and each one is usually designed for a specific problem, we believe this survey can provide a broad overview of the challenges in ocular biometrics.

     

A new thermal infrared and visible spectrum images-based pedestrian detection system

In this paper, we propose a hybrid system for pedestrian detection, in which both thermal and visible images of the same scene are used. The proposed method is achieved in two basic steps: (1) Hypotheses generation (HG) where the locations of possible pedestrians in an image are determined and (2) hypotheses verification (HV), where tests are done to check the presence of pedestrians in the generated hypotheses. HG step segments the thermal image using a modified version of OTSU thresholding technique. The segmentation results are mapped into the corresponding visible image to obtain the regions of interests (possible pedestrians). A post-processing is done on the resulting regions of interests to keep only significant ones. HV is performed using random forest as classifier and a color-based histogram of oriented gradients (HOG) together with the histograms of oriented optical flow (HOOF) as features. The proposed approach has been tested on OSU Color-Thermal, INO Video Analytics and LITIV data sets and the results justify its effectiveness.

     

A novel visible spectrum images-based pedestrian detection and tracking system for surveillance in non-controlled environments

Multimedia Tools and Applications - For many vision applications, robust detection and tracking of pedestrians in image sequences are essential. In this paper, a hybrid system for pedestrian...     

Smartcards and biometrics: an overview

The security aspects of more than 50 different smartcard-based security systems have been investigated by the Evaluation Centre. These investigations comprise physical security aspects (the ‘silicon’), logical security aspects (card operating systems) and organizational measures (e.g. transport, initialization).     

Multimodal soft biometrics: combining ear and face biometrics for age and gender classification

Multimedia Tools and Applications - In this paper, we present a multimodal, multitask deep convolutional neural network framework for age and gender classification. In the developed framework, we...     

Fractional biometrics: safeguarding privacy in biometric applications

This paper presents a biometric system solution that “masks” a fraction of a person’s biometric image before submission, to reduce the possibility of forgery and collusion. A prototype system was constructed for the fingerprint biometric and tested in three security scenarios. It is shown that implementing the fractional biometric system does not significantly affect accuracy. We provide theoretical security analysis on the guessing entropy of a Fractional Template and the security against collusion. We demonstrate that by masking above 50% of the biometric features, we achieve a sufficient mix of security, robustness and accuracy to warrant further study. When 75% of the features are masked, we found that the theoretical guessing entropy is 42 bits, and we found that, on average, 5 authenticators had to collude before the system would be compromised.